Contextual dependencies in unsupervised word segmentation

Sharon Goldwater; Thomas L. Griffiths; Mark Johnson

doi:10.3115/1220175.1220260

Contextual dependencies in unsupervised word segmentation

Sharon Goldwater, Thomas L. Griffiths, Mark Johnson

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

151 Scopus citations

Abstract

Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on suboptimal search procedures.

Original language	English (US)
Title of host publication	COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Publisher	Association for Computational Linguistics (ACL)
Pages	673-680
Number of pages	8
ISBN (Print)	1932432655, 9781932432657
DOIs	https://doi.org/10.3115/1220175.1220260
State	Published - 2006
Externally published	Yes
Event	21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 - Sydney, NSW, Australia Duration: Jul 17 2006 → Jul 21 2006

Publication series

Name	COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference
Volume	1

Other

Other	21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006
Country/Territory	Australia
City	Sydney, NSW
Period	7/17/06 → 7/21/06

All Science Journal Classification (ASJC) codes

Language and Linguistics
Linguistics and Language

Access to Document

10.3115/1220175.1220260

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Goldwater, S., Griffiths, T. L., & Johnson, M. (2006). Contextual dependencies in unsupervised word segmentation. In COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference (pp. 673-680). (COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference; Vol. 1). Association for Computational Linguistics (ACL). https://doi.org/10.3115/1220175.1220260

Goldwater, Sharon ; Griffiths, Thomas L. ; Johnson, Mark. / Contextual dependencies in unsupervised word segmentation. COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2006. pp. 673-680 (COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference).

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title = "Contextual dependencies in unsupervised word segmentation",

abstract = "Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on suboptimal search procedures.",

author = "Sharon Goldwater and Griffiths, {Thomas L.} and Mark Johnson",

year = "2006",

doi = "10.3115/1220175.1220260",

language = "English (US)",

isbn = "1932432655",

series = "COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",

publisher = "Association for Computational Linguistics (ACL)",

pages = "673--680",

booktitle = "COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference",

note = "21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006 ; Conference date: 17-07-2006 Through 21-07-2006",

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Goldwater, S, Griffiths, TL & Johnson, M 2006, Contextual dependencies in unsupervised word segmentation. in COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference, vol. 1, Association for Computational Linguistics (ACL), pp. 673-680, 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, COLING/ACL 2006, Sydney, NSW, Australia, 7/17/06. https://doi.org/10.3115/1220175.1220260

Contextual dependencies in unsupervised word segmentation. / Goldwater, Sharon; Griffiths, Thomas L.; Johnson, Mark.
COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL), 2006. p. 673-680 (COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference; Vol. 1).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AB - Developing better methods for segmenting continuous text into words is important for improving the processing of Asian languages, and may shed light on how humans learn to segment speech. We propose two new Bayesian word segmentation methods that assume unigram and bigram models of word dependencies respectively. The bigram model greatly outperforms the unigram model (and previous probabilistic models), demonstrating the importance of such dependencies for word segmentation. We also show that previous probabilistic models rely crucially on suboptimal search procedures.

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Goldwater S, Griffiths TL, Johnson M. Contextual dependencies in unsupervised word segmentation. In COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference. Association for Computational Linguistics (ACL). 2006. p. 673-680. (COLING/ACL 2006 - 21st International Conference on Computational Linguistics and 44th Annual Meeting of the Association for Computational Linguistics, Proceedings of the Conference). doi: 10.3115/1220175.1220260

Contextual dependencies in unsupervised word segmentation

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